Overview

Dataset statistics

Number of variables35
Number of observations51808
Missing cells91040
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 MiB
Average record size in memory280.0 B

Variable types

Categorical21
Numeric14

Alerts

AMTI is highly overall correlated with VALUEHigh correlation
CONFEE is highly overall correlated with IFFEE and 1 other fieldsHigh correlation
ZSMHC is highly overall correlated with ZINC2 and 2 other fieldsHigh correlation
HHAGE is highly overall correlated with QSS and 1 other fieldsHigh correlation
ZINC2 is highly overall correlated with ZSMHC and 1 other fieldsHigh correlation
ZINC is highly overall correlated with ZSMHC and 1 other fieldsHigh correlation
ZINCN is highly overall correlated with ZINCHHigh correlation
VALUE is highly overall correlated with AMTI and 1 other fieldsHigh correlation
UNITSF is highly overall correlated with ROOMSHigh correlation
ROOMS is highly overall correlated with UNITSFHigh correlation
CELLAR is highly overall correlated with MOBILTYPHigh correlation
CLIMB is highly overall correlated with CONDO and 1 other fieldsHigh correlation
IFFEE is highly overall correlated with CONFEEHigh correlation
ZINCH is highly overall correlated with ZINCNHigh correlation
QSS is highly overall correlated with HHAGEHigh correlation
QRETIR is highly overall correlated with HHAGEHigh correlation
CONDO is highly overall correlated with CONFEE and 1 other fieldsHigh correlation
MOBILTYP is highly overall correlated with CELLAR and 1 other fieldsHigh correlation
BUYI is highly imbalanced (76.5%)Imbalance
QRENT is highly imbalanced (58.5%)Imbalance
CONDO is highly imbalanced (64.4%)Imbalance
TYPE is highly imbalanced (86.7%)Imbalance
EROACH is highly imbalanced (52.5%)Imbalance
CRACKS is highly imbalanced (72.7%)Imbalance
HOLES is highly imbalanced (93.9%)Imbalance
IFFEE has 1352 (2.6%) missing valuesMissing
ZINCH has 3329 (6.4%) missing valuesMissing
MOBILTYP has 49868 (96.3%) missing valuesMissing
FRSTOC has 36308 (70.1%) missing valuesMissing
CLIMB has 970 (1.9%) zerosZeros

Reproduction

Analysis started2023-04-28 12:40:42.723226
Analysis finished2023-04-28 12:41:04.725984
Duration22 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

BUYI
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
1
49814 
0
 
1994

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 49814
96.2%
0 1994
 
3.8%

Length

2023-04-28T14:41:04.758223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:04.820109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 49814
96.2%
0 1994
 
3.8%

Most occurring characters

ValueCountFrequency (%)
1 49814
96.2%
0 1994
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49814
96.2%
0 1994
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49814
96.2%
0 1994
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49814
96.2%
0 1994
 
3.8%

AMTI
Real number (ℝ)

Distinct1874
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean911.11739
Minimum-6
Maximum5582
Zeros0
Zeros (%)0.0%
Negative1994
Negative (%)3.8%
Memory size404.9 KiB
2023-04-28T14:41:04.879922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile75
Q1450
median700
Q31104
95-th percentile2400
Maximum5582
Range5588
Interquartile range (IQR)654

Descriptive statistics

Standard deviation836.46251
Coefficient of variation (CV)0.91806228
Kurtosis12.70692
Mean911.11739
Median Absolute Deviation (MAD)300
Skewness3.0173433
Sum47203170
Variance699669.53
MonotonicityNot monotonic
2023-04-28T14:41:04.950110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 3004
 
5.8%
1200 2957
 
5.7%
1000 2158
 
4.2%
800 2143
 
4.1%
-6 1994
 
3.8%
900 1783
 
3.4%
500 1674
 
3.2%
700 1613
 
3.1%
300 1162
 
2.2%
1500 1148
 
2.2%
Other values (1864) 32172
62.1%
ValueCountFrequency (%)
-6 1994
3.8%
1 208
 
0.4%
2 30
 
0.1%
3 5
 
< 0.1%
4 2
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
8 2
 
< 0.1%
9 8
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
5582 739
1.4%
3960 1
 
< 0.1%
3905 1
 
< 0.1%
3900 36
 
0.1%
3888 1
 
< 0.1%
3880 2
 
< 0.1%
3872 1
 
< 0.1%
3840 1
 
< 0.1%
3800 16
 
< 0.1%
3795 5
 
< 0.1%

CONFEE
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.243283
Minimum-9
Maximum1110
Zeros0
Zeros (%)0.0%
Negative40001
Negative (%)77.2%
Memory size404.9 KiB
2023-04-28T14:41:05.024014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile-6
Q1-6
median-6
Q3-6
95-th percentile185
Maximum1110
Range1119
Interquartile range (IQR)0

Descriptive statistics

Standard deviation107.90492
Coefficient of variation (CV)4.4509205
Kurtosis61.840926
Mean24.243283
Median Absolute Deviation (MAD)0
Skewness7.0033505
Sum1255996
Variance11643.473
MonotonicityNot monotonic
2023-04-28T14:41:05.093557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6 39180
75.6%
15 1438
 
2.8%
25 1394
 
2.7%
5 1113
 
2.1%
35 913
 
1.8%
-9 821
 
1.6%
45 757
 
1.5%
55 628
 
1.2%
65 437
 
0.8%
105 322
 
0.6%
Other values (43) 4805
 
9.3%
ValueCountFrequency (%)
-9 821
 
1.6%
-6 39180
75.6%
5 1113
 
2.1%
15 1438
 
2.8%
25 1394
 
2.7%
35 913
 
1.8%
45 757
 
1.5%
55 628
 
1.2%
65 437
 
0.8%
75 274
 
0.5%
ValueCountFrequency (%)
1110 316
0.6%
495 6
 
< 0.1%
485 11
 
< 0.1%
475 12
 
< 0.1%
465 8
 
< 0.1%
455 31
 
0.1%
445 13
 
< 0.1%
435 19
 
< 0.1%
425 22
 
< 0.1%
415 20
 
< 0.1%

ZSMHC
Real number (ℝ)

Distinct5299
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1495.4224
Minimum0
Maximum12595
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size404.9 KiB
2023-04-28T14:41:05.166109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile328
Q1675
median1228
Q31910
95-th percentile3603
Maximum12595
Range12595
Interquartile range (IQR)1235

Descriptive statistics

Standard deviation1182.4481
Coefficient of variation (CV)0.79071179
Kurtosis8.7707943
Mean1495.4224
Median Absolute Deviation (MAD)598
Skewness2.3562949
Sum77474843
Variance1398183.5
MonotonicityNot monotonic
2023-04-28T14:41:05.245210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
430 46
 
0.1%
483 44
 
0.1%
521 44
 
0.1%
418 44
 
0.1%
497 44
 
0.1%
567 42
 
0.1%
563 42
 
0.1%
459 42
 
0.1%
467 41
 
0.1%
385 41
 
0.1%
Other values (5289) 51378
99.2%
ValueCountFrequency (%)
0 4
< 0.1%
2 2
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
25 1
 
< 0.1%
29 1
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
12595 1
< 0.1%
12084 1
< 0.1%
10424 1
< 0.1%
10409 1
< 0.1%
10316 1
< 0.1%
10307 1
< 0.1%
10280 1
< 0.1%
10058 1
< 0.1%
9992 1
< 0.1%
9901 1
< 0.1%

IFFEE
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1352
Missing (%)2.6%
Memory size404.9 KiB
2.0
37977 
1.0
12479 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters151368
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 37977
73.3%
1.0 12479
 
24.1%
(Missing) 1352
 
2.6%

Length

2023-04-28T14:41:05.307857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:05.360352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 37977
75.3%
1.0 12479
 
24.7%

Most occurring characters

ValueCountFrequency (%)
. 50456
33.3%
0 50456
33.3%
2 37977
25.1%
1 12479
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100912
66.7%
Other Punctuation 50456
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50456
50.0%
2 37977
37.6%
1 12479
 
12.4%
Other Punctuation
ValueCountFrequency (%)
. 50456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 50456
33.3%
0 50456
33.3%
2 37977
25.1%
1 12479
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 50456
33.3%
0 50456
33.3%
2 37977
25.1%
1 12479
 
8.2%

HHAGE
Real number (ℝ)

Distinct76
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.412658
Minimum15
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.9 KiB
2023-04-28T14:41:05.416992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile30
Q143
median54
Q365
95-th percentile82
Maximum93
Range78
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.495271
Coefficient of variation (CV)0.28477329
Kurtosis-0.54614331
Mean54.412658
Median Absolute Deviation (MAD)11
Skewness0.23414626
Sum2819011
Variance240.10344
MonotonicityNot monotonic
2023-04-28T14:41:05.488803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 1417
 
2.7%
55 1309
 
2.5%
54 1289
 
2.5%
51 1272
 
2.5%
53 1265
 
2.4%
52 1261
 
2.4%
56 1213
 
2.3%
57 1203
 
2.3%
47 1192
 
2.3%
49 1185
 
2.3%
Other values (66) 39202
75.7%
ValueCountFrequency (%)
15 1
 
< 0.1%
16 1
 
< 0.1%
17 5
 
< 0.1%
18 6
 
< 0.1%
19 13
 
< 0.1%
20 19
 
< 0.1%
21 45
 
0.1%
22 44
 
0.1%
23 100
0.2%
24 152
0.3%
ValueCountFrequency (%)
93 478
0.9%
89 173
 
0.3%
88 212
0.4%
87 233
0.4%
86 274
0.5%
85 321
0.6%
84 340
0.7%
83 365
0.7%
82 354
0.7%
81 437
0.8%

HHSEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
1
28984 
2
22824 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 28984
55.9%
2 22824
44.1%

Length

2023-04-28T14:41:05.555662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:05.608324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 28984
55.9%
2 22824
44.1%

Most occurring characters

ValueCountFrequency (%)
1 28984
55.9%
2 22824
44.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28984
55.9%
2 22824
44.1%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28984
55.9%
2 22824
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28984
55.9%
2 22824
44.1%

ZINC2
Real number (ℝ)

Distinct23314
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88454.481
Minimum-344
Maximum2977104
Zeros163
Zeros (%)0.3%
Negative458
Negative (%)0.9%
Memory size404.9 KiB
2023-04-28T14:41:05.665502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-344
5-th percentile9062.45
Q134194.25
median65840
Q3110996
95-th percentile235500
Maximum2977104
Range2977448
Interquartile range (IQR)76801.75

Descriptive statistics

Standard deviation97041.51
Coefficient of variation (CV)1.0970785
Kurtosis72.247007
Mean88454.481
Median Absolute Deviation (MAD)35986
Skewness5.8387141
Sum4.5826497 × 109
Variance9.4170546 × 109
MonotonicityNot monotonic
2023-04-28T14:41:05.740575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 255
 
0.5%
60000 240
 
0.5%
30000 235
 
0.5%
25000 233
 
0.4%
100000 224
 
0.4%
40000 213
 
0.4%
-4 171
 
0.3%
75000 168
 
0.3%
80000 167
 
0.3%
0 163
 
0.3%
Other values (23304) 49739
96.0%
ValueCountFrequency (%)
-344 1
 
< 0.1%
-140 1
 
< 0.1%
-129 5
 
< 0.1%
-123 2
 
< 0.1%
-86 17
< 0.1%
-84 1
 
< 0.1%
-82 8
 
< 0.1%
-56 3
 
< 0.1%
-43 22
< 0.1%
-42 5
 
< 0.1%
ValueCountFrequency (%)
2977104 1
< 0.1%
1918796 1
< 0.1%
1918740 1
< 0.1%
1838552 1
< 0.1%
1775035 1
< 0.1%
1644107 1
< 0.1%
1638552 1
< 0.1%
1613524 1
< 0.1%
1593552 1
< 0.1%
1588552 1
< 0.1%

ZINC
Real number (ℝ)

Distinct22689
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86345.711
Minimum-344
Maximum2977104
Zeros181
Zeros (%)0.3%
Negative509
Negative (%)1.0%
Memory size404.9 KiB
2023-04-28T14:41:05.819030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-344
5-th percentile8375.35
Q132316
median63596
Q3109000
95-th percentile231899.65
Maximum2977104
Range2977448
Interquartile range (IQR)76684

Descriptive statistics

Standard deviation96103.212
Coefficient of variation (CV)1.113005
Kurtosis73.363026
Mean86345.711
Median Absolute Deviation (MAD)36376
Skewness5.8736185
Sum4.4733986 × 109
Variance9.2358273 × 109
MonotonicityNot monotonic
2023-04-28T14:41:05.891551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 272
 
0.5%
60000 269
 
0.5%
30000 257
 
0.5%
25000 253
 
0.5%
40000 239
 
0.5%
100000 233
 
0.4%
-4 201
 
0.4%
0 181
 
0.3%
80000 173
 
0.3%
75000 173
 
0.3%
Other values (22679) 49557
95.7%
ValueCountFrequency (%)
-344 1
 
< 0.1%
-140 1
 
< 0.1%
-129 4
 
< 0.1%
-123 2
 
< 0.1%
-86 19
< 0.1%
-84 1
 
< 0.1%
-82 8
 
< 0.1%
-56 3
 
< 0.1%
-43 24
< 0.1%
-42 5
 
< 0.1%
ValueCountFrequency (%)
2977104 1
< 0.1%
1918796 1
< 0.1%
1918740 1
< 0.1%
1838552 1
< 0.1%
1775035 1
< 0.1%
1644107 1
< 0.1%
1638552 1
< 0.1%
1593552 1
< 0.1%
1588552 1
< 0.1%
1538752 1
< 0.1%

ZINCN
Real number (ℝ)

Distinct778
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11254.151
Minimum-2049
Maximum410311
Zeros0
Zeros (%)0.0%
Negative45369
Negative (%)87.6%
Memory size404.9 KiB
2023-04-28T14:41:05.969211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2049
5-th percentile-8
Q1-6
median-6
Q3-6
95-th percentile80000
Maximum410311
Range412360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation43345.652
Coefficient of variation (CV)3.8515257
Kurtosis48.27922
Mean11254.151
Median Absolute Deviation (MAD)0
Skewness6.2373896
Sum5.8305505 × 108
Variance1.8788456 × 109
MonotonicityNot monotonic
2023-04-28T14:41:06.136554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6 38881
75.0%
-8 2930
 
5.7%
-7 2372
 
4.6%
-9 898
 
1.7%
410311 346
 
0.7%
-2049 288
 
0.6%
100000 239
 
0.5%
50000 230
 
0.4%
60000 203
 
0.4%
70000 192
 
0.4%
Other values (768) 5229
 
10.1%
ValueCountFrequency (%)
-2049 288
 
0.6%
-9 898
 
1.7%
-8 2930
 
5.7%
-7 2372
 
4.6%
-6 38881
75.0%
2 27
 
0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
10 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
410311 346
0.7%
209000 1
 
< 0.1%
208000 1
 
< 0.1%
207130 1
 
< 0.1%
206800 1
 
< 0.1%
206000 2
 
< 0.1%
205000 3
 
< 0.1%
204000 1
 
< 0.1%
203600 2
 
< 0.1%
203000 1
 
< 0.1%

ZINCH
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3329
Missing (%)6.4%
Memory size404.9 KiB
1.0
38881 
2.0
9598 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters145437
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 38881
75.0%
2.0 9598
 
18.5%
(Missing) 3329
 
6.4%

Length

2023-04-28T14:41:06.200619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:06.253289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 38881
80.2%
2.0 9598
 
19.8%

Most occurring characters

ValueCountFrequency (%)
. 48479
33.3%
0 48479
33.3%
1 38881
26.7%
2 9598
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96958
66.7%
Other Punctuation 48479
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48479
50.0%
1 38881
40.1%
2 9598
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 48479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145437
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 48479
33.3%
0 48479
33.3%
1 38881
26.7%
2 9598
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 48479
33.3%
0 48479
33.3%
1 38881
26.7%
2 9598
 
6.6%

QSS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
36535 
1
15273 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 36535
70.5%
1 15273
29.5%

Length

2023-04-28T14:41:06.302388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:06.354339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 36535
70.5%
1 15273
29.5%

Most occurring characters

ValueCountFrequency (%)
2 36535
70.5%
1 15273
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 36535
70.5%
1 15273
29.5%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 36535
70.5%
1 15273
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 36535
70.5%
1 15273
29.5%

QSELF
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
44575 
1
7233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 44575
86.0%
1 7233
 
14.0%

Length

2023-04-28T14:41:06.399922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:06.452136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 44575
86.0%
1 7233
 
14.0%

Most occurring characters

ValueCountFrequency (%)
2 44575
86.0%
1 7233
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 44575
86.0%
1 7233
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 44575
86.0%
1 7233
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 44575
86.0%
1 7233
 
14.0%

QRENT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
47476 
1
 
4332

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 47476
91.6%
1 4332
 
8.4%

Length

2023-04-28T14:41:06.497873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:06.553073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 47476
91.6%
1 4332
 
8.4%

Most occurring characters

ValueCountFrequency (%)
2 47476
91.6%
1 4332
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 47476
91.6%
1 4332
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 47476
91.6%
1 4332
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 47476
91.6%
1 4332
 
8.4%

QRETIR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
41557 
1
10251 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 41557
80.2%
1 10251
 
19.8%

Length

2023-04-28T14:41:06.602047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:06.666614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 41557
80.2%
1 10251
 
19.8%

Most occurring characters

ValueCountFrequency (%)
2 41557
80.2%
1 10251
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 41557
80.2%
1 10251
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 41557
80.2%
1 10251
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 41557
80.2%
1 10251
 
19.8%

VALUE
Real number (ℝ)

Distinct1273
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266946.45
Minimum1
Maximum5264699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.9 KiB
2023-04-28T14:41:06.734223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40000
Q1105000
median180000
Q3300000
95-th percentile775000
Maximum5264699
Range5264698
Interquartile range (IQR)195000

Descriptive statistics

Standard deviation317216.91
Coefficient of variation (CV)1.1883166
Kurtosis57.277587
Mean266946.45
Median Absolute Deviation (MAD)90000
Skewness5.680097
Sum1.3829962 × 1010
Variance1.0062657 × 1011
MonotonicityNot monotonic
2023-04-28T14:41:06.814138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 2160
 
4.2%
150000 1997
 
3.9%
100000 1673
 
3.2%
250000 1573
 
3.0%
300000 1557
 
3.0%
120000 1265
 
2.4%
400000 1059
 
2.0%
80000 1056
 
2.0%
90000 1020
 
2.0%
180000 1014
 
2.0%
Other values (1263) 37434
72.3%
ValueCountFrequency (%)
1 49
0.1%
2 3
 
< 0.1%
5 6
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
15 1
 
< 0.1%
24 1
 
< 0.1%
50 3
 
< 0.1%
70 4
 
< 0.1%
100 10
 
< 0.1%
ValueCountFrequency (%)
5264699 22
< 0.1%
4414135 13
< 0.1%
4168257 22
< 0.1%
3688473 20
< 0.1%
3500000 2
 
< 0.1%
3450000 1
 
< 0.1%
3400000 1
 
< 0.1%
3300000 1
 
< 0.1%
3200000 1
 
< 0.1%
3100000 2
 
< 0.1%

REGION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
4
17124 
3
15408 
2
13503 
1
5773 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 17124
33.1%
3 15408
29.7%
2 13503
26.1%
1 5773
 
11.1%

Length

2023-04-28T14:41:06.885314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:06.965589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 17124
33.1%
3 15408
29.7%
2 13503
26.1%
1 5773
 
11.1%

Most occurring characters

ValueCountFrequency (%)
4 17124
33.1%
3 15408
29.7%
2 13503
26.1%
1 5773
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 17124
33.1%
3 15408
29.7%
2 13503
26.1%
1 5773
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 17124
33.1%
3 15408
29.7%
2 13503
26.1%
1 5773
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 17124
33.1%
3 15408
29.7%
2 13503
26.1%
1 5773
 
11.1%

METRO3
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
39037 
1
11928 
9
 
843

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 39037
75.3%
1 11928
 
23.0%
9 843
 
1.6%

Length

2023-04-28T14:41:07.023026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:07.081671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 39037
75.3%
1 11928
 
23.0%
9 843
 
1.6%

Most occurring characters

ValueCountFrequency (%)
2 39037
75.3%
1 11928
 
23.0%
9 843
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39037
75.3%
1 11928
 
23.0%
9 843
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 39037
75.3%
1 11928
 
23.0%
9 843
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 39037
75.3%
1 11928
 
23.0%
9 843
 
1.6%

CONDO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
3
48321 
1
 
3487

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 48321
93.3%
1 3487
 
6.7%

Length

2023-04-28T14:41:07.132670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:07.193711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 48321
93.3%
1 3487
 
6.7%

Most occurring characters

ValueCountFrequency (%)
3 48321
93.3%
1 3487
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 48321
93.3%
1 3487
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 48321
93.3%
1 3487
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 48321
93.3%
1 3487
 
6.7%

UNITSF
Real number (ℝ)

Distinct2405
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2122.776
Minimum-8
Maximum20159
Zeros0
Zeros (%)0.0%
Negative4515
Negative (%)8.7%
Memory size404.9 KiB
2023-04-28T14:41:07.252301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-7
Q11200
median1800
Q32500
95-th percentile4300
Maximum20159
Range20167
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation2021.8382
Coefficient of variation (CV)0.95245008
Kurtosis25.584094
Mean2122.776
Median Absolute Deviation (MAD)600
Skewness4.1925942
Sum1.0997678 × 108
Variance4087829.7
MonotonicityNot monotonic
2023-04-28T14:41:07.335042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7 4453
 
8.6%
2000 2308
 
4.5%
1200 2267
 
4.4%
1800 2173
 
4.2%
1500 2049
 
4.0%
1600 1583
 
3.1%
1400 1506
 
2.9%
3000 1393
 
2.7%
2400 1331
 
2.6%
2200 1318
 
2.5%
Other values (2395) 31427
60.7%
ValueCountFrequency (%)
-8 62
 
0.1%
-7 4453
8.6%
99 15
 
< 0.1%
100 22
 
< 0.1%
110 5
 
< 0.1%
120 3
 
< 0.1%
122 1
 
< 0.1%
128 1
 
< 0.1%
140 1
 
< 0.1%
144 2
 
< 0.1%
ValueCountFrequency (%)
20159 77
0.1%
19591 71
0.1%
16286 68
0.1%
14846 57
0.1%
12999 72
0.1%
12364 71
0.1%
11849 69
0.1%
10202 73
0.1%
10144 75
0.1%
10027 89
0.2%

LOT
Real number (ℝ)

Distinct2564
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40938.471
Minimum-6
Maximum933185
Zeros0
Zeros (%)0.0%
Negative4248
Negative (%)8.2%
Memory size404.9 KiB
2023-04-28T14:41:07.415440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile-6
Q15500
median10120
Q322000
95-th percentile176000
Maximum933185
Range933191
Interquartile range (IQR)16500

Descriptive statistics

Standard deviation115456.52
Coefficient of variation (CV)2.8202452
Kurtosis28.309134
Mean40938.471
Median Absolute Deviation (MAD)5880
Skewness5.1450556
Sum2.1209403 × 109
Variance1.3330209 × 1010
MonotonicityNot monotonic
2023-04-28T14:41:07.497000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 4359
 
8.4%
5500 4294
 
8.3%
-6 4248
 
8.2%
22000 3191
 
6.2%
44000 2839
 
5.5%
5000 1506
 
2.9%
14000 1479
 
2.9%
33000 1458
 
2.8%
6000 1100
 
2.1%
10000 1010
 
1.9%
Other values (2554) 26324
50.8%
ValueCountFrequency (%)
-6 4248
8.2%
200 36
 
0.1%
220 1
 
< 0.1%
225 5
 
< 0.1%
230 1
 
< 0.1%
240 5
 
< 0.1%
242 1
 
< 0.1%
250 5
 
< 0.1%
256 2
 
< 0.1%
292 1
 
< 0.1%
ValueCountFrequency (%)
933185 67
0.1%
926349 59
0.1%
816872 70
0.1%
810031 68
0.1%
805341 56
0.1%
772696 78
0.2%
759755 64
0.1%
728341 54
0.1%
722618 65
0.1%
718692 54
0.1%

ROOMS
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5768607
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.9 KiB
2023-04-28T14:41:07.559376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q38
95-th percentile10
Maximum15
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6813889
Coefficient of variation (CV)0.25565219
Kurtosis0.83175592
Mean6.5768607
Median Absolute Deviation (MAD)1
Skewness0.66993903
Sum340734
Variance2.8270686
MonotonicityNot monotonic
2023-04-28T14:41:07.611453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 13083
25.3%
7 10571
20.4%
5 10204
19.7%
8 7458
14.4%
9 3719
 
7.2%
4 3642
 
7.0%
10 1548
 
3.0%
11 615
 
1.2%
3 480
 
0.9%
12 284
 
0.5%
Other values (5) 204
 
0.4%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 24
 
< 0.1%
3 480
 
0.9%
4 3642
 
7.0%
5 10204
19.7%
6 13083
25.3%
7 10571
20.4%
8 7458
14.4%
9 3719
 
7.2%
10 1548
 
3.0%
ValueCountFrequency (%)
15 8
 
< 0.1%
14 67
 
0.1%
13 101
 
0.2%
12 284
 
0.5%
11 615
 
1.2%
10 1548
 
3.0%
9 3719
 
7.2%
8 7458
14.4%
7 10571
20.4%
6 13083
25.3%

CELLAR
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9632875
Minimum-6
Maximum5
Zeros0
Zeros (%)0.0%
Negative4252
Negative (%)8.2%
Memory size404.9 KiB
2023-04-28T14:41:07.759852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile-6
Q11
median3
Q34
95-th percentile4
Maximum5
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6804306
Coefficient of variation (CV)1.3652766
Kurtosis3.5360702
Mean1.9632875
Median Absolute Deviation (MAD)1
Skewness-1.9885346
Sum101714
Variance7.1847081
MonotonicityNot monotonic
2023-04-28T14:41:07.811141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 17391
33.6%
1 14673
28.3%
3 10046
19.4%
2 4793
 
9.3%
-6 4252
 
8.2%
5 653
 
1.3%
ValueCountFrequency (%)
-6 4252
 
8.2%
1 14673
28.3%
2 4793
 
9.3%
3 10046
19.4%
4 17391
33.6%
5 653
 
1.3%
ValueCountFrequency (%)
5 653
 
1.3%
4 17391
33.6%
3 10046
19.4%
2 4793
 
9.3%
1 14673
28.3%
-6 4252
 
8.2%

MOBILTYP
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing49868
Missing (%)96.3%
Memory size404.9 KiB
2.0
1013 
1.0
927 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5820
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 1013
 
2.0%
1.0 927
 
1.8%
(Missing) 49868
96.3%

Length

2023-04-28T14:41:07.865592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:07.921460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1013
52.2%
1.0 927
47.8%

Most occurring characters

ValueCountFrequency (%)
. 1940
33.3%
0 1940
33.3%
2 1013
17.4%
1 927
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3880
66.7%
Other Punctuation 1940
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1940
50.0%
2 1013
26.1%
1 927
23.9%
Other Punctuation
ValueCountFrequency (%)
. 1940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1940
33.3%
0 1940
33.3%
2 1013
17.4%
1 927
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1940
33.3%
0 1940
33.3%
2 1013
17.4%
1 927
15.9%

TYPE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
1
49536 
2
 
1692
9
 
308
3
 
253
7
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 49536
95.6%
2 1692
 
3.3%
9 308
 
0.6%
3 253
 
0.5%
7 19
 
< 0.1%

Length

2023-04-28T14:41:07.969678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.026377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 49536
95.6%
2 1692
 
3.3%
9 308
 
0.6%
3 253
 
0.5%
7 19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 49536
95.6%
2 1692
 
3.3%
9 308
 
0.6%
3 253
 
0.5%
7 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49536
95.6%
2 1692
 
3.3%
9 308
 
0.6%
3 253
 
0.5%
7 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49536
95.6%
2 1692
 
3.3%
9 308
 
0.6%
3 253
 
0.5%
7 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49536
95.6%
2 1692
 
3.3%
9 308
 
0.6%
3 253
 
0.5%
7 19
 
< 0.1%

BUILT
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.8761
Minimum1919
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.9 KiB
2023-04-28T14:41:08.079828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1919
5-th percentile1920
Q11950
median1975
Q31990
95-th percentile2006
Maximum2011
Range92
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.523757
Coefficient of variation (CV)0.012950462
Kurtosis-0.7533469
Mean1970.8761
Median Absolute Deviation (MAD)20
Skewness-0.44126915
Sum1.0210715 × 108
Variance651.46216
MonotonicityNot monotonic
2023-04-28T14:41:08.138106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1960 6295
12.2%
1950 6058
11.7%
1995 4326
 
8.4%
1985 3962
 
7.6%
1975 3870
 
7.5%
1970 3812
 
7.4%
1990 3736
 
7.2%
1980 2899
 
5.6%
1940 2859
 
5.5%
1919 2310
 
4.5%
Other values (14) 11681
22.5%
ValueCountFrequency (%)
1919 2310
 
4.5%
1920 1733
 
3.3%
1930 1497
 
2.9%
1940 2859
5.5%
1950 6058
11.7%
1960 6295
12.2%
1970 3812
7.4%
1975 3870
7.5%
1980 2899
5.6%
1985 3962
7.6%
ValueCountFrequency (%)
2011 97
 
0.2%
2010 277
 
0.5%
2009 326
 
0.6%
2008 511
1.0%
2007 716
1.4%
2006 926
1.8%
2005 1079
2.1%
2004 851
1.6%
2003 864
1.7%
2002 846
1.6%

CLIMB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.678428
Minimum-6
Maximum21
Zeros970
Zeros (%)1.9%
Negative49501
Negative (%)95.5%
Memory size404.9 KiB
2023-04-28T14:41:08.195948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile-6
Q1-6
median-6
Q3-6
95-th percentile-6
Maximum21
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5569972
Coefficient of variation (CV)-0.2741951
Kurtosis39.489382
Mean-5.678428
Median Absolute Deviation (MAD)0
Skewness5.5524762
Sum-294188
Variance2.4242402
MonotonicityNot monotonic
2023-04-28T14:41:08.260849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-6 49501
95.5%
0 970
 
1.9%
1 740
 
1.4%
2 349
 
0.7%
3 121
 
0.2%
4 37
 
0.1%
5 20
 
< 0.1%
6 12
 
< 0.1%
8 10
 
< 0.1%
13 9
 
< 0.1%
Other values (11) 39
 
0.1%
ValueCountFrequency (%)
-6 49501
95.5%
0 970
 
1.9%
1 740
 
1.4%
2 349
 
0.7%
3 121
 
0.2%
4 37
 
0.1%
5 20
 
< 0.1%
6 12
 
< 0.1%
7 6
 
< 0.1%
8 10
 
< 0.1%
ValueCountFrequency (%)
21 4
< 0.1%
20 2
 
< 0.1%
19 1
 
< 0.1%
16 4
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 9
< 0.1%
12 4
< 0.1%
11 8
< 0.1%
10 3
 
< 0.1%

FRSTOC
Categorical

Distinct2
Distinct (%)< 0.1%
Missing36308
Missing (%)70.1%
Memory size404.9 KiB
1.0
7973 
2.0
7527 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters46500
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 7973
 
15.4%
2.0 7527
 
14.5%
(Missing) 36308
70.1%

Length

2023-04-28T14:41:08.318137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.394422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7973
51.4%
2.0 7527
48.6%

Most occurring characters

ValueCountFrequency (%)
. 15500
33.3%
0 15500
33.3%
1 7973
17.1%
2 7527
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31000
66.7%
Other Punctuation 15500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15500
50.0%
1 7973
25.7%
2 7527
24.3%
Other Punctuation
ValueCountFrequency (%)
. 15500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15500
33.3%
0 15500
33.3%
1 7973
17.1%
2 7527
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15500
33.3%
0 15500
33.3%
1 7973
17.1%
2 7527
16.2%

EVROD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
41848 
1
9960 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 41848
80.8%
1 9960
 
19.2%

Length

2023-04-28T14:41:08.444890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.500927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 41848
80.8%
1 9960
 
19.2%

Most occurring characters

ValueCountFrequency (%)
2 41848
80.8%
1 9960
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 41848
80.8%
1 9960
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 41848
80.8%
1 9960
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 41848
80.8%
1 9960
 
19.2%

EROACH
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
46522 
1
5286 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 46522
89.8%
1 5286
 
10.2%

Length

2023-04-28T14:41:08.547486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.601058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 46522
89.8%
1 5286
 
10.2%

Most occurring characters

ValueCountFrequency (%)
2 46522
89.8%
1 5286
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 46522
89.8%
1 5286
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 46522
89.8%
1 5286
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 46522
89.8%
1 5286
 
10.2%

CRACKS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
49378 
1
 
2430

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 49378
95.3%
1 2430
 
4.7%

Length

2023-04-28T14:41:08.647213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.709355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 49378
95.3%
1 2430
 
4.7%

Most occurring characters

ValueCountFrequency (%)
2 49378
95.3%
1 2430
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49378
95.3%
1 2430
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49378
95.3%
1 2430
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49378
95.3%
1 2430
 
4.7%

HOLES
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
51438 
1
 
370

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 51438
99.3%
1 370
 
0.7%

Length

2023-04-28T14:41:08.754204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.806834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 51438
99.3%
1 370
 
0.7%

Most occurring characters

ValueCountFrequency (%)
2 51438
99.3%
1 370
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 51438
99.3%
1 370
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 51438
99.3%
1 370
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 51438
99.3%
1 370
 
0.7%

WINTERNONE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing183
Missing (%)0.4%
Memory size404.9 KiB
1.0
41895 
2.0
9730 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters154875
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 41895
80.9%
2.0 9730
 
18.8%
(Missing) 183
 
0.4%

Length

2023-04-28T14:41:08.851439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:08.904647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 41895
81.2%
2.0 9730
 
18.8%

Most occurring characters

ValueCountFrequency (%)
. 51625
33.3%
0 51625
33.3%
1 41895
27.1%
2 9730
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103250
66.7%
Other Punctuation 51625
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51625
50.0%
1 41895
40.6%
2 9730
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 51625
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 154875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 51625
33.3%
0 51625
33.3%
1 41895
27.1%
2 9730
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 154875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 51625
33.3%
0 51625
33.3%
1 41895
27.1%
2 9730
 
6.3%

AIR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
2
43203 
1
8605 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 43203
83.4%
1 8605
 
16.6%

Length

2023-04-28T14:41:08.952180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:09.007043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 43203
83.4%
1 8605
 
16.6%

Most occurring characters

ValueCountFrequency (%)
2 43203
83.4%
1 8605
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 43203
83.4%
1 8605
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 43203
83.4%
1 8605
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 43203
83.4%
1 8605
 
16.6%

AIRSYS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
1
39810 
2
11998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 39810
76.8%
2 11998
 
23.2%

Length

2023-04-28T14:41:09.053465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:41:09.105958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 39810
76.8%
2 11998
 
23.2%

Most occurring characters

ValueCountFrequency (%)
1 39810
76.8%
2 11998
 
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39810
76.8%
2 11998
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common 51808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39810
76.8%
2 11998
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39810
76.8%
2 11998
 
23.2%

Interactions

2023-04-28T14:41:02.895097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:49.625591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:50.562113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:51.557294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:54.934036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.121593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.056912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.105650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.093448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.089581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.022485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.993577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:03.086479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:49.835935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:53.918034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.085055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.253072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.194566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.248937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.225586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.225633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.160666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.236168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:03.220403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:49.973098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:55.160818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.323711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.262654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.321510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.292772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:50.959270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:51.963524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:54.058700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.237064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.397425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.330137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.401030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.359751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.362693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.297883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.376897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:03.347795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:50.104805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:51.021370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:52.027780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:52.976844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.155168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.311408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.464081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.394875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.468554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.421827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.426804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.359951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:53.044937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.245452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.382989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.528586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.473567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.538625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.486076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.492772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.424523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.505038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:03.482060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:50.241411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:51.156622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:52.166204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:53.119634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.348420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.458797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.602539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.549486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.615771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.555938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:50.305656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:52.230116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:53.184999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.480971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.530912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.664956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.613894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.687386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.618327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.628756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.558965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.640372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:03.612320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:50.371741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:51.367026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:52.297859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:53.253094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.567787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.608698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.729956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.685176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.757761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.684237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.696136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.625821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.706627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:50.435396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:52.363857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:53.499460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.638105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.687497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.793861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.753211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-04-28T14:40:59.747934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.761228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.689943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.770133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:03.742037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:50.498376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:51.492865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:52.429111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:53.569980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:54.705853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:55.919741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:56.860065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:57.816608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:58.898359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:40:59.810490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:00.824385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:01.775984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T14:41:02.831464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-04-28T14:41:09.176332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
AMTICONFEEZSMHCHHAGEZINC2ZINCZINCNVALUEUNITSFLOTROOMSCELLARBUILTCLIMBBUYIIFFEEHHSEXZINCHQSSQSELFQRENTQRETIRREGIONMETRO3CONDOMOBILTYPTYPEFRSTOCEVRODEROACHCRACKSHOLESWINTERNONEAIRAIRSYS
AMTI1.0000.0410.400-0.0010.3120.3140.0670.5280.3600.1380.3500.1520.079-0.0950.2710.0340.0560.0000.0420.1060.1070.0240.1050.0360.1430.2420.0820.0290.0330.0590.0320.0280.0120.0820.045
CONFEE0.0411.0000.225-0.0700.1470.1470.1320.1850.136-0.2250.0390.0600.4010.2160.0420.5380.0410.0160.0400.0070.0070.0290.1170.0350.6870.0550.0900.0090.0750.0390.0250.0060.0220.0800.069
ZSMHC0.4000.2251.000-0.3760.5250.5190.0990.5680.3700.0050.3730.1340.253-0.0160.1220.1960.0750.0670.2950.1410.1000.1850.1710.0290.0160.1910.0750.0000.0270.0520.0230.0220.0110.1130.072
HHAGE-0.001-0.070-0.3761.000-0.294-0.278-0.085-0.010-0.0570.019-0.067-0.041-0.1900.0100.0410.0950.0780.1440.7800.1130.0410.5070.0380.0320.0720.1450.0220.1360.0660.0190.0370.0120.0200.0260.080
ZINC20.3120.1470.525-0.2941.0000.9800.0830.4670.3770.0860.3710.0720.203-0.0490.0230.0710.0360.0000.0670.0970.0860.0440.0490.0150.0160.0000.0140.0000.0000.0100.0090.0000.0130.0500.032
ZINC0.3140.1470.519-0.2780.9801.0000.0820.4670.3810.0940.3760.0730.208-0.0530.0230.0710.0360.0040.0660.0960.0860.0440.0480.0150.0170.0000.0130.0020.0000.0090.0100.0000.0140.0490.034
ZINCN0.0670.1320.099-0.0850.0830.0821.0000.0440.022-0.0220.0170.0030.0170.0010.0340.0720.0250.6630.1190.0600.0270.0640.0340.0200.0190.0770.0180.0200.0210.0130.0240.0000.0310.0290.032
VALUE0.5280.1850.568-0.0100.4670.4670.0441.0000.4520.0790.4220.1710.182-0.0470.0300.0530.0280.0000.0000.1000.1270.0120.2040.0280.0220.0000.0310.0120.0210.0500.0220.0000.0000.0880.129
UNITSF0.3600.1360.370-0.0570.3770.3810.0220.4521.0000.2850.6040.1010.320-0.1850.0990.1700.0910.0000.0670.1050.0750.0290.1230.0910.1380.1980.0680.0900.0180.0370.0350.0200.0350.1310.180
LOT0.138-0.2250.0050.0190.0860.094-0.0220.0790.2851.0000.281-0.0040.090-0.3440.0240.1070.0390.0150.0330.0450.0380.0280.0860.0970.0750.0970.0400.0920.1020.0390.0130.0000.0060.0200.038
ROOMS0.3500.0390.373-0.0670.3710.3760.0170.4220.6040.2811.0000.0830.192-0.2340.1620.1580.0740.0090.0880.1120.0730.0440.0420.0700.2710.4610.1280.0440.0430.0060.0180.0270.0060.0960.164
CELLAR0.1520.0600.134-0.0410.0720.0730.0030.1710.101-0.0040.0831.0000.198-0.3560.2010.2210.0480.0190.0590.0400.0490.0360.3780.0860.3731.0000.3350.0710.1160.1260.0210.0500.0680.1310.171
BUILT0.0790.4010.253-0.1900.2030.2080.0170.1820.3200.0900.1920.1981.000-0.0400.0610.4660.0550.0270.1760.0370.0720.1030.1660.1850.1520.1370.0760.3030.1520.0870.0860.0320.1060.2870.371
CLIMB-0.0950.216-0.0160.010-0.049-0.0530.001-0.047-0.185-0.344-0.234-0.356-0.0401.0000.0390.2100.0350.0000.0210.0160.0870.0110.0570.0840.5241.0000.0790.0270.0490.0160.0000.0000.0120.0330.113
BUYI0.2710.0420.1220.0410.0230.0230.0340.0300.0990.0240.1620.2010.0610.0391.0000.0420.0180.0000.0080.0240.0240.0360.0660.0410.0360.2720.2460.0260.0210.0490.0640.0820.0290.0970.115
IFFEE0.0340.5380.1960.0950.0710.0710.0720.0530.1700.1070.1580.2210.4660.2100.0421.0000.0000.0270.0600.0170.0080.0230.1650.0970.4320.0290.1250.0140.1120.0470.0470.0180.0350.1760.173
HHSEX0.0560.0410.0750.0780.0360.0360.0250.0280.0910.0390.0740.0480.0550.0350.0180.0001.0000.0130.0630.0520.0200.0000.0130.0430.0510.0000.0350.0270.0040.0000.0160.0100.0240.0110.023
ZINCH0.0000.0160.0670.1440.0000.0040.6630.0000.0000.0150.0090.0190.0270.0000.0000.0270.0131.0000.1090.0700.0170.0720.0270.0090.0060.0440.0000.0140.0220.0220.0320.0180.0420.0160.000
QSS0.0420.0400.2950.7800.0670.0660.1190.0000.0670.0330.0880.0590.1760.0210.0080.0600.0630.1091.0000.0930.0170.4850.0370.0260.0270.1060.0450.0520.0280.0000.0280.0060.0030.0230.052
QSELF0.1060.0070.1410.1130.0970.0960.0600.1000.1050.0450.1120.0400.0370.0160.0240.0170.0520.0700.0931.0000.0930.0770.0610.0170.0180.0080.0280.0000.0390.0100.0000.0090.0300.0100.007
QRENT0.1070.0070.1000.0410.0860.0860.0270.1270.0750.0380.0730.0490.0720.0870.0240.0080.0200.0170.0170.0931.0000.0100.0900.0220.0100.0830.0410.0090.0230.0000.0000.0000.0210.0000.050
QRETIR0.0240.0290.1850.5070.0440.0440.0640.0120.0290.0280.0440.0360.1030.0110.0360.0230.0000.0720.4850.0770.0101.0000.0450.0280.0190.1370.0110.0450.0260.0120.0280.0120.0050.0000.008
REGION0.1050.1170.1710.0380.0490.0480.0340.2040.1230.0860.0420.3780.1660.0570.0660.1650.0130.0270.0370.0610.0900.0451.0000.1490.1380.3000.0400.0670.0440.2910.0380.0150.0440.2280.370
METRO30.0360.0350.0290.0320.0150.0150.0200.0280.0910.0970.0700.0860.1850.0840.0410.0970.0430.0090.0260.0170.0220.0280.1491.0000.0600.0000.0410.0310.0140.1020.0470.0150.0360.0710.120
CONDO0.1430.6870.0160.0720.0160.0170.0190.0220.1380.0750.2710.3730.1520.5240.0360.4320.0510.0060.0270.0180.0100.0190.1380.0601.0000.0000.2080.0220.0710.0310.0220.0090.0200.0630.008
MOBILTYP0.2420.0550.1910.1450.0000.0000.0770.0000.1980.0970.4611.0000.1371.0000.2720.0290.0000.0440.1060.0080.0830.1370.3000.0000.0001.0000.0130.1180.0000.1120.0730.0740.0860.2300.186
TYPE0.0820.0900.0750.0220.0140.0130.0180.0310.0680.0400.1280.3350.0760.0790.2460.1250.0350.0000.0450.0280.0410.0110.0400.0410.2080.0131.0000.0030.0280.0140.0240.0710.0270.0770.049
FRSTOC0.0290.0090.0000.1360.0000.0020.0200.0120.0900.0920.0440.0710.3030.0270.0260.0140.0270.0140.0520.0000.0090.0450.0670.0310.0220.1180.0031.0000.0230.0400.0140.0070.0110.0260.015
EVROD0.0330.0750.0270.0660.0000.0000.0210.0210.0180.1020.0430.1160.1520.0490.0210.1120.0040.0220.0280.0390.0230.0260.0440.0140.0710.0000.0280.0231.0000.1070.1020.0480.1300.1000.086
EROACH0.0590.0390.0520.0190.0100.0090.0130.0500.0370.0390.0060.1260.0870.0160.0490.0470.0000.0220.0000.0100.0000.0120.2910.1020.0310.1120.0140.0400.1071.0000.1040.0510.0740.0450.048
CRACKS0.0320.0250.0230.0370.0090.0100.0240.0220.0350.0130.0180.0210.0860.0000.0640.0470.0160.0320.0280.0000.0000.0280.0380.0470.0220.0730.0240.0140.1020.1041.0000.2040.0880.0820.061
HOLES0.0280.0060.0220.0120.0000.0000.0000.0000.0200.0000.0270.0500.0320.0000.0820.0180.0100.0180.0060.0090.0000.0120.0150.0150.0090.0740.0710.0070.0480.0510.2041.0000.0430.0490.042
WINTERNONE0.0120.0220.0110.0200.0130.0140.0310.0000.0350.0060.0060.0680.1060.0120.0290.0350.0240.0420.0030.0300.0210.0050.0440.0360.0200.0860.0270.0110.1300.0740.0880.0431.0000.1220.094
AIR0.0820.0800.1130.0260.0500.0490.0290.0880.1310.0200.0960.1310.2870.0330.0970.1760.0110.0160.0230.0100.0000.0000.2280.0710.0630.2300.0770.0260.1000.0450.0820.0490.1221.0000.496
AIRSYS0.0450.0690.0720.0800.0320.0340.0320.1290.1800.0380.1640.1710.3710.1130.1150.1730.0230.0000.0520.0070.0500.0080.3700.1200.0080.1860.0490.0150.0860.0480.0610.0420.0940.4961.000

Missing values

2023-04-28T14:41:03.866689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-28T14:41:04.285240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-28T14:41:04.650828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BUYIAMTICONFEEZSMHCIFFEEHHAGEHHSEXZINC2ZINCZINCNZINCHQSSQSELFQRENTQRETIRVALUEREGIONMETRO3CONDOUNITSFLOTROOMSCELLARMOBILTYPTYPEBUILTCLIMBFRSTOCEVRODEROACHCRACKSHOLESWINTERNONEAIRAIRSYS
01160011542401.03421599721599721000002.022227200004233000590084NaN12002-62.022222.021
11165022535021.0431156772156772-61.022225500004211600-654NaN12002-61.021121.021
212160-650141.060114884961488496-9NaN212272000042325005900114NaN12001-61.022221.021
3190021546091.03711249441249441250002.022224500004231600200054NaN12001-62.022221.021
4114006548911.03321499721499721750002.021227000004232750600094NaN11995-62.022221.021
51100016549191.04513494434944-61.022227400004232350550064NaN12002-62.022221.021
611650-640952.0351258644258644-61.022225500004231901600074NaN12003-61.011121.021
712800-653102.0501310916310916-61.0222213000004235700650094NaN12003-61.022221.021
81270017568661.0381561216561216-61.022121500000423570010000104NaN12003-61.022221.021
9180041533491.07223870038700-61.012221030004232300900064NaN12003-62.022221.021
BUYIAMTICONFEEZSMHCIFFEEHHAGEHHSEXZINC2ZINCZINCNZINCHQSSQSELFQRENTQRETIRVALUEREGIONMETRO3CONDOUNITSFLOTROOMSCELLARMOBILTYPTYPEBUILTCLIMBFRSTOCEVRODEROACHCRACKSHOLESWINTERNONEAIRAIRSYS
517981698-637662.0421161998161998-61.02222340000123200017600071NaN11990-62.012221.021
51799166029510871.08021250012500-61.012222200001211412-641NaN11990-61.022221.021
51800113216511241.056110199856999-61.022221320001211000-651NaN91990-62.022221.021
518011600-625532.043227000270001200002.0212228500012322503300071NaN11990-62.012221.021
5180211200-617162.04916499864998-61.02222200000123190026400061NaN11990-61.022221.021
518031550-69792.05119419694196-61.0222216000012321004400062NaN11970-6NaN22221.021
518041800-612772.07026603066030-61.012211750001233000350081NaN11970-6NaN22221.021
518050-6-65172.06822499824999-61.012221500012390025005-61.021995-61.022221.012
518060-6-64262.0712131324000-61.012221500012378025004-61.021960-6NaN22222.022
518071200-65682.08023300033000-61.0122121500123147084006-61.022001-62.022221.022